Hybrid PSO-AMLS-based method for data fitting in the calibration of the infrared radiometer

A hybrid PSO-AMLS-based method for data fitting in the calibration of the infrared radiometer was described. The proposed method was based on Particle Swarm Optimization (PSO) in combination with Adaptive Moving Least Squares (AMLS). The optimization technique involved parameters setting in the AMLS fitting, which significantly influenced the fitting accuracy. However, its use in the calibration of the infrared radiometer has not been yet widely explored. Bearing this in mind, the PSO-AMLS-based method, which was based on the local approximation scheme, was successfully used here to get the relationship between the radiation of the standard source and the output voltage of the infrared radiometer. The main advantages of this method were the flexible adjustment mechanism in data processing and the ability in reducing the adverse effect resulting from the non-uniform distribution of fitting data. Numerical examples and experiments are performed to verify the superior performance of the PSO-AMLS-based method compared to the conventional data fitting method.